Wavelet Transform With Histogram-Based Threshold Estimation for Online Partial Discharge Signal Denoising

Online condition assessment of the power system devices and apparatus is considered vital for robust operation, where partial discharge (PD) detection is employed as a diagnosis tool. PD measurements, however, are corrupted with different types of noises such as white noise, random noise, and discrete spectral interferences. Hence, the denoising of such corrupted PD signals remains a challenging problem in PD signal detection and classification. The challenge lies in removing these noises from the online PD signal measurements effectively, while retaining its discriminant features and characteristics. In this paper, wavelet-based denoising with a new histogram-based threshold function and selection rule is proposed. The proposed threshold estimation technique obtains two different threshold values for each wavelet sub-band and uses a prodigious thresholding function that conserves the original signal energy. Moreover, two signal-to-noise ratio (SNR) estimation techniques are derived to fit with actual PD signals corrupted with real noise. The proposed technique is applied on different acoustic and current measured PD signals to examine its performance under different noisy environments. The simulation results confirm the merits of the proposed denoising technique compared with other existing wavelet-based techniques by measuring four evaluation metrics: 1) SNR; 2) cross-correlation coefficient; 3) mean square error; and 4) reduction in noise level.